TidyDensity (version 1.5.0)

util_hypergeometric_aic: Calculate Akaike Information Criterion (AIC) for Hypergeometric Distribution

Description

This function estimates the parameters m, n, and k of a hypergeometric distribution from the provided data and then calculates the AIC value based on the fitted distribution.

Usage

util_hypergeometric_aic(.x)

Value

The AIC value calculated based on the fitted hypergeometric distribution to the provided data.

Arguments

.x

A numeric vector containing the data to be fitted to a hypergeometric distribution.

Author

Steven P. Sanderson II, MPH

Details

This function calculates the Akaike Information Criterion (AIC) for a hypergeometric distribution fitted to the provided data.

This function fits a hypergeometric distribution to the provided data. It estimates the parameters m, n, and k of the hypergeometric distribution from the data. Then, it calculates the AIC value based on the fitted distribution.

Initial parameter estimates: The function does not estimate parameters; they are directly calculated from the data.

Optimization method: Since the parameters are directly calculated from the data, no optimization is needed.

Goodness-of-fit: While AIC is a useful metric for model comparison, it's recommended to also assess the goodness-of-fit of the chosen model using visualization and other statistical tests.

See Also

Other Utility: check_duplicate_rows(), convert_to_ts(), quantile_normalize(), tidy_mcmc_sampling(), util_beta_aic(), util_binomial_aic(), util_cauchy_aic(), util_chisq_aic(), util_exponential_aic(), util_f_aic(), util_gamma_aic(), util_generalized_beta_aic(), util_generalized_pareto_aic(), util_geometric_aic(), util_inverse_burr_aic(), util_inverse_pareto_aic(), util_inverse_weibull_aic(), util_logistic_aic(), util_lognormal_aic(), util_negative_binomial_aic(), util_normal_aic(), util_paralogistic_aic(), util_pareto1_aic(), util_pareto_aic(), util_poisson_aic(), util_t_aic(), util_triangular_aic(), util_uniform_aic(), util_weibull_aic(), util_zero_truncated_binomial_aic(), util_zero_truncated_geometric_aic(), util_zero_truncated_negative_binomial_aic(), util_zero_truncated_poisson_aic()

Examples

# Example 1: Calculate AIC for a sample dataset
set.seed(123)
x <- rhyper(100, m = 10, n = 10, k = 5)
util_hypergeometric_aic(x)